Results 11 to 20 of about 2,481,944 (297)
Modernizing use of regression models in physics education research: A review of hierarchical linear modeling [PDF]
Physical Review Physics Education Research, 2019 [This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] Physics education researchers (PER) often analyze student data with single-level regression models (e.g., linear and logistic regression).
Ben Van Dusen, Jayson Nissen
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HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models in R
Journal of Statistical Software, 2014 Over the last twenty years there have been numerous developments in diagnostic pro- cedures for hierarchical linear models; however, these procedures are not widely imple- mented in statistical software packages, and those packages that do contain a ...
Adam Loy, Heike Hofmann
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Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python [PDF]
Journal of Statistical Software, 2022 The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications. This is the result of a variety of methodological advances with faster and cheaper hardware as well as the ...
Tomás Capretto +5 more
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G-optimal designs for hierarchical linear models: an equivalence theorem and a nature-inspired meta-heuristic algorithm. [PDF]
Soft comput, 2021 Hierarchical linear models are widely used in many research disciplines and estimation issues for such models are generally well addressed. Design issues are relatively much less discussed for hierarchical linear models but there is an increasing ...
Liu X, Yue R, Zhang Z, Wong WK.
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Optimal shrinkage estimation in heteroscedastic hierarchical linear models [PDF]
, 2015 Shrinkage estimators have profound impacts in statistics and in scientific and engineering applications. In this article, we consider shrinkage estimation in the presence of linear predictors.
Kou, Samuel, Yang, Justin J.
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Data Analysis Using Hierarchical Generalized Linear Models with R
Journal of Statistical Software, 2019 Carmen Armero
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Hierarchical Generalized Linear Models
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996 SUMMARY We consider hierarchical generalized linear models which allow extra error components in the linear predictors of generalized linear models. The distribution of these components is not restricted to be normal; this allows a broader class of models, which includes generalized linear mixed models.
Y. Lee, J. A. Nelder
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Frontiers in Psychology, 2017 We investigated the effects of violations of the sphericity assumption on Type I error rates for different methodical approaches of repeated measures analysis using a simulation approach.
Nicolas Haverkamp, André Beauducel
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Increased interpretation of deep learning models using hierarchical cluster-based modelling.
PLoS ONE, 2023 Linear prediction models based on data with large inhomogeneity or abrupt non-linearities often perform poorly because relationships between groups in the data dominate the model.
Elise Lunde Gjelsvik, Kristin Tøndel
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The supervised hierarchical Dirichlet process [PDF]
, 2014 We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group.
Dai, Andrew M., Storkey, Amos J.
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